基于SMPA的半挂车自动泊车运动规划方法研究

doi:10.19562/j.chinasae.qcgc.2024.04.015

Abstract

Abstract:

The unstable kinematic characteristics of tractor-semitrailer vehicles bring considerable challenges for autonomous motion planning in the parking process. In this paper， sequential motion planning algorithm （SMPA） is proposed to address the problems of low efficiency of parking motion planning algorithms and poor smoothness of results for tractor-semitrailer in static scenario with multiple obstacles. Firstly， an initial path generation method based on the quadratic planning strategy and improved bidirectional rapidly-exploring random tree algorithm （Bi-RRT） is proposed. Then， combined with the study of path node feasibility discrimination method under vehicle non-complete differential constraints， a probability-based target bias sampling strategy is proposed to improve the sampling efficiency. Finally， a nonlinear optimal control model oriented to the continuity of the control variables of the vehicle system is constructed to solve the docking problem of the parking reversal point and improve the parking trajectory smoothness. Simulation results show that this method reduces the planning time by 86.71% and 21.44% compared to Hybrid A* and Bi-RRT， respectively， in multi obstacle scenarios， with more superior trajectory quality.

Key words: tractor-semitrailer automatic parking, quadratic programming, improved Bi-RRT, nonlinear optimal control model

Yuanmin Wang,Yafei Wang,Wengang Qin,Hao Chen,Yinhua Liu. Study on Automatic Parking Motion Planning Method for Tractor-Semitrailer Based on SMPA[J].Automotive Engineering, 2024, 46(4): 691-702.

Figures/Tables 19

参数	数值
$L 0$ /m	4.42
$L 1$ /m	7.9
$L h$ /m	0.1
$W 0$ /m	2.2
$L f 0$ /m	1.36
$L f 1$ /m	8
$L r 0$ /m	1.5
$L r 1$ /m	3.06
$δ f m a x$ /rad	0.7
$v 0 m a x$ /（km·h^-1）	5
$γ m a x$ /rad	1.05

References 19

1	交通运输智慧物流标准体系建设指南发布［J］.标准生活，2022（6）：7.
	Transportation and intelligent logistics standard system construction guidelines released［J］. Standard Life，2022（6）：7.
2	唐永兴，朱战霞，张红文，等.机器人运动规划方法综述［J］.航空学报，2023，44（2）：181-212.
	TANG Y X， ZHU Z X， ZHANG H W， et al. A tutorial and review on robot motion planning［J］. Chinese Journal of Aeronautics， 2023，44（2）：181-212.
3	吉望宁.半挂车自主泊车路径规划及控制策略研究［D］.长春：吉林大学，2022.
	JI W N. Research on path planning and control strategy of semi-trailer for automated valet parking［D］. Changchun： Jilin University，2022.
4	胡满江，牟斌杰，杨泽宇，等.基于DBSCAN与二分法的混合A*路径规划方法［J］. 汽车工程，2023，45（3）：341-349，371.
	HU M J， MOU B J， YANG Z Y， et al. A hybrid A* path planning method based on DBSCAN and dichotomy［J］. Automotive Engineering， 2023，45（3）：341-349，371.
5	姚智龙，张小俊，王金刚.改进Bi-RRT*算法的自动泊车路径规划［J/OL］.机械科学与技术：1-10［2023-07-18］.https：//doi.org/10.13433/j.cnki.1003-8728.20220310.
	YAO Z L， ZHANG X J， WANG J G. Improved Bi-RRT* automatic parking path planning algorithm［J/OL］. Mechanical Science and Technology for Aerospace Engineering： 1-10［2023-07-18］.https：//doi.org/10.13433/j.cnki.1003-8728.20220310.
6	VAILLAND G， GOURANTON V， BABEL M. Cubic bézier local path planner for non-holonomic feasible and comfortable path generation［C］. 2021 IEEE International Conference on Robotics and Automation （ICRA）. IEEE， 2021： 7894-7900.
7	唐晓峰，杨林，袁静妮. 基于高斯伪谱法的自动驾驶车辆状态研究［J］. 汽车工程， 2020， 42（5）： 567-573.
	TANG X F， YANG L， YUAN J N. Study on the states of autonomous vehicle based on Gaussian pseudospectral method［J］. Automotive Engineering，2020，42（5）： 567-573.
8	LI B， SHAO Z. A unified motion planning method for parking an autonomous vehicle in the presence of irregularly placed obstacles［J］. Knowledge-Based Systems， 2015， 86： 11-20.
9	LJUNGQVIST O， EVESTEDT N， AXEHILL D， et al. A path planning and path-following control framework for a general 2‐trailer with a car-like tractor［J］. Journal of Field Robotics， 2019， 36（8）： 1345-1377.
10	MOHAMED A， REN J， LANG H， et al. Optimal path planning for an autonomous articulated vehicle with two trailers［J］. International Journal of Automation and Control， 2018， 12（3）： 449-465.
11	BUKKA G V. Path planning algorithm for driver assistance during complex maneuvering of articulated vehicles［D］. Rhine-Waal University of Applied Sciences， 2020.
12	李柏. 复杂约束下自动驾驶车辆运动规划的计算最优控制方法研究［D］. 杭州：浙江大学， 2018.
	LI B. Research on computational optimal control methods for automated vehicle motion planning problems with complicated constraints［D］. Hangzhou： Zhejiang University， 2018.
13	MANAV A C， LAZOGLU I. A novel cascade path planning algorithm for autonomous truck-trailer parking［J］. IEEE Transactions on Intelligent Transportation Systems， 2021， 23（7）： 6821-6835.
14	BERGMAN K， LJUNGQVIST O， AXEHILL D. Improved path planning by tightly combining lattice-based path planning and optimal control［J］. IEEE Transactions on Intelligent Vehicles， 2020， 6（1）： 57-66.
15	CHAI R， TSOURDOS A， SAVVARIS A， et al. Two-stage trajectory optimization for autonomous ground vehicles parking maneuver［J］. IEEE Transactions on Industrial Informatics， 2018， 15（7）： 3899-3909.
16	LI B， ACARMAN T， ZHANG Y， et al. Tractor-trailer vehicle trajectory planning in narrow environments with a progressively constrained optimal control approach［J］. IEEE Transactions on Intelligent Vehicles， 2019， 5（3）： 414-425.
17	SAMANIEGO R， RODRÍGUEZ R， VÁZQUEZ F， et al. Efficient path planing for articulated vehicles in cluttered environments［J］. Sensors， 2020， 20（23）： 6821.
18	ZIEGLER J， STILLER C. Fast collision checking for intelligent vehicle motion planning［C］. 2010 IEEE Intelligent Vehicles Symposium. IEEE， 2010： 518-522.
19	YANG K， SUKKARIEH S. An analytical continuous-curvature path-smoothing algorithm［J］. IEEE Transactions on Robotics， 2010， 26（3）： 561-568.

路障数量	Bi-RSc-RRT		Hybrid A*		PCOC（ref. incl.）		SMPA
路障数量	成功率/%	CPU时间/s	成功率/%	CPU时间/s	成功率/%	CPU时间/s	成功率/%	CPU时间^*/s
0	100	1.67	100	10.46	100	46.12	100	1.13
2	100	3.51	100	15.82	70	69.75	100	3.06
3	70	3.78	100	16.30	0		90	3.58
4	70	5.97	100	35.29	0		80	4.69

Study on Automatic Parking Motion Planning Method for Tractor-Semitrailer Based on SMPA

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 19

References 19

Related Articles 0

Metrics

Comments

Recommended 0

算法	Bi-RSc-RRT	Hybrid A*	SMPA
前轮转角突变次数	6	7	0
前进后退切换次数	1	3	1